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Computer Shop Clerk(5 years)
IT Development Manager(10 years)
SQL DBA/Developer(5 years)
Data Warehouse Developer/Junior Manager(6 years)
Data Warehouse Manager(5 years)
Global Data Integration Senior Manager(4 years)
Head of Data ScienceAnd BIG Data Solutions(1 year)
About me
1980s 1990s 2000s 2010s 2017
1980‘80s
‘90s‘00s
‘10s“My goal is to use predictive analytics in conjunction with scenario based algorithms to produce prescriptive analytics and actionable events.”
“My goal was to use aggregated reports to produce KPI reports andto use predictive models to estimate future growth.”
“My goal was to aggregate and integrate data and offer analytics based on different segmentations like time or geography .”
“My goal was to produce ad-hoc reports for a large variety ofdepartments using a centralized media for distribution.”
“My goal was to produce reports.”
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Descriptive Analytics
Diagnostic
Analytics
Predictive Analytics
Prescriptive Analytics
What happened
Roll-ups and how, when and where
Identify problems and fire alerts
Why and forecasting
What will happen if…Next best action
Max vs. Analytics in the years:
Data assurance Data availability
Data fit to analytical purpose
WHAT HAPPENED ?
We overloaded the lake with raw and meaningless information That added overhead to the process of discovering, integrating
and transforming/aggregating data That also fragmented the tools required to do the job Times for POCs, demonstrations, quick wins, tactical and low
hanging fruits went up exponentially due to heavy data wrangling We lost faith in data modelling, data architecture, metadata and
data assurance Because of that there has been a huge proliferation of data
outside of the lake
WHY DID IT HAPPEN ?
Agile methodologies applied at delivery level and not from the top down Poor understanding of data assets, no idea where data is Poor or no ODS strategy Lack of product owners, data stewards and data governance Poor master data and reference data management Adoption of a raw data layer with no system refined data Application of security and regulations on top of existing landscape Poor data definitions and integration Lack of data modelling and poor data design and architecture Lack of a metadata and data quality strategy Complex security models
Adopt SAFe as a scaled agile methodology, funnel projects don’t sieve them Know your data from source and make sure it is fit for analytical purposes Start a data governance and stewardship program Adoption of data modelling as an enterprise tool, again Adoption of a data assurance strategy Adoption of a raw layer and a refined layer in your logical data warehouse or
data lake Adoption of data lineage and metadata capturing Improve data availability Standardize your platforms and development tools Provide better or ODS functionality Consider a dedicated Chief Data Office In a large enterprise consider a dedicated Chief Analytics Office Security and regulations must be part of the data fabricHOW DO I GET BACK ON TRACK ?
SOME MORE SPECIFIC ADVICE
DIGITAL TRANSFORMATION CHALLENGES
Adoption Resistance is the major challenge here 80% of the time is about people and not technology
Vision Needs to be clearly communicated and supported by senior management down to delivery
Data It’s imperative to identify and back up 100% data sources that are going to be vital to the Digital strategy
and vision Technology
Needs to be clear what technology stack will be at the core of the delivery of the vision Execution
Needs to be sharp once adoption takes place This is where a lot of transformations of this type really fail
ChallengeLook back at
operations and processes and challenge the
status quo and the old way to
do things.
TransformRe-invent
where necessary,
disrupt when it makes sense,
change what is not sustainable
anymore.
ExecutePut an action
against everything that
needs to be done and act
accordingly. No procrastination.
ENABLERS TO A DIGITAL VISION
Head of Data
Head of Data Governance and
Architecture
Head of Data Management and
Design
Head of Data Security and Integration
Head of Analytics and Visualization
Data Architects
Governance
Data Modellers
Metadata
Design
Security
EQLT
Stewardship Master Data MI
Data Science
Big Data
Architecture
BI & Monitoring
Infra
stru
ctur
e
Regulatory Risk
BUILDING YOUR CHIEF DATA OFFICE
QualityPresentationMonitoringModellingConnectionIngestion
Engagement
Procurement Integration (EQLT) Consumption
Acquisition AnalyticsVisualization
Data, Master Data and Metadata Management
Data Architecture and DesignLogical Model
Physical Model Functional
ModelDATADEFINITIONDOCUMENT
STTM
Data Governance and StewardshipConceptual
Model
SECURITYMANIFESTO
BUILDING YOUR EXECUTION LAYER
Scientific Model
CV
LinkedInProfile
Cover Letter
ML
NLP
Job Specs
AIMatches
WEB UI
BOT
Mobile
EmployersEmployees
WebServicesAPIs
BUILDING A REAL TIME SOLUTION (TO DISRUPT THE RECRUITMENT INDUSTRY)
EXCEL VS GDPR
EXCEL: WHERE DATA DIES
Hard to share and reconcile Can be anywhere on any PC, on any drive, company wide Will result in discrepancies over KPIs and metrics Will go up in size very easily if you work on historical data Will contain intelligence only available at worksheet level Cannot contribute to a digital strategy for lack of integration
and automation
SOME ADVICE WHEN IT COMES TO A BI STRATEGY
Centralize code and formulas Consider ODS in your strategy Focus on availability and sharing the insights and metrics across
various platforms Less fragmentation of visualization tools Focus on data integration to favour natural segmentation of your data Think about ingestion of data and what format better suits your BI
strategy Where possible, stay away from Excel !
Customers
Batc
h
Refe
renc
e
Real
Tim
e
Digi
tal C
hann
el NBA
NBA
NBA
NBA
NBANBA
NBA
NBA
NBA
NBA
NBA
NBA
NBA
NBA
NBA
NBA
THE DATA RIVER
